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介绍

在与疾病相关的细胞类型中绘制增强子和靶基因图谱，能够为全基因组关联研究（GWAS）变异的功能机制提供关键见解。单细胞多模态数据能够测量同一细胞中的基因表达和染色质可及性，从而实现细胞类型特异性地推断增强子 - 基因对。然而，这项任务受到数据稀疏性、测序深度变化以及分析大量对子所带来计算负担的挑战。我们引入了 scMultiMap 这一统计方法，它利用联合潜在变量模型从稀疏多模态计数中推断增强子 - 基因关联。它能够校正技术混杂因素，允许快速基于矩的估计，并提供通过解析推导得出的 p 值。在血液和大脑数据中，scMultiMap 显示出适当的 I 型错误控制、高统计功效和计算效率（仅为现有方法的 1%）。当应用于阿尔茨海默病（AD）数据时，scMultiMap 在小胶质细胞中给出了最高的遗传力富集，并揭示了有关 AD GWAS 变异的调控机制的见解。

Mapping enhancers and target genes in disease-related cell types provides critical insights into the functional mechanisms of genome-wide association studies (GWAS) variants. Single-cell multimodal data, which measure gene expression and chromatin accessibility in the same cells, enable the cell-type-specific inference of enhancer-gene pairs. However, this task is challenged by high data sparsity, sequencing depth variation, and the computational burden of analyzing a large number of pairs. We introduce scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p-values. In blood and brain data, scMultiMap shows appropriate type I error control, high statistical power, and computational efficiency (1% of existing methods). When applied to Alzheimer’s disease (AD) data, scMultiMap gives the highest heritability enrichment in microglia and reveals insights into the regulatory mechanisms of AD GWAS variants.

代码

https://changsubiostats.github.io/scMultiMap/articles/disease_control.html

参考

• scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data
• https://changsubiostats.github.io/scMultiMap/articles/disease_control.html